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Coded Elastic Computing. (arXiv:1812.06411v2 [cs.IT] UPDATED)
来源于:arXiv
Cloud providers have recently introduced low-priority machines to reduce the
cost of computations. Exploiting such opportunity for machine learning tasks is
challenging inasmuch as low-priority machines can elastically leave (through
preemption) and join the computation at any time. In this paper, we design a
new technique called coded elastic computing enabling distributed machine
learning computations over elastic resources. The proposed technique allows
machines to transparently leave the computation without sacrificing the
algorithm-level performance, and, at the same time, flexibly reduce the
workload at existing machines when new machines join the computation. Thanks to
the redundancy provided by encoding, our approach is able to achieve similar
computational cost as the original (uncoded) method when all machines are
present; the cost gracefully increases when machines are preempted and reduces
when machines join. We test the performance of the proposed technique on two
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